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1. Federated Learning Enables Big Data for Rare Cancer Boundary Detection

Author

Pati, Sarthak, Baid, Ujjwal, Edwards, Brandon, Sheller, Micah, Wang, Shih-Han, Reina, G Anthony, Foley, Patrick, Gruzdev, Alexey, Karkada, Deepthi, Davatzikos, Christos, Sako, Chiharu, Ghodasara, Satyam, Bilello, Michel, Mohan, Suyash, Vollmuth, Philipp, Brugnara, Gianluca, Preetha, Chandrakanth J, Sahm, Felix, Maier-Hein, Klaus, Zenk, Maximilian, Bendszus, Martin, Wick, Wolfgang, Calabrese, Evan, Rudie, Jeffrey, Villanueva-Meyer, Javier, Cha, Soonmee, Ingalhalikar, Madhura, Jadhav, Manali, Pandey, Umang, Saini, Jitender, Garrett, John, Larson, Matthew, Jeraj, Robert, Currie, Stuart, Frood, Russell, Fatania, Kavi, Huang, Raymond Y, Chang, Ken, Balana, Carmen, Capellades, Jaume, Puig, Josep, Trenkler, Johannes, Pichler, Josef, Necker, Georg, Haunschmidt, Andreas, Meckel, Stephan, Shukla, Gaurav, Liem, Spencer, Alexander, Gregory S, Lombardo, Joseph, Palmer, Joshua D, Flanders, Adam E, Dicker, Adam P, Sair, Haris I, Jones, Craig K, Venkataraman, Archana, Jiang, Meirui, So, Tiffany Y, Chen, Cheng, Heng, Pheng Ann, Dou, Qi, Kozubek, Michal, Lux, Filip, Michálek, Jan, Matula, Petr, Keřkovský, Miloš, Kopřivová, Tereza, Dostál, Marek, Vybíhal, Václav, Vogelbaum, Michael A, Mitchell, J Ross, Farinhas, Joaquim, Maldjian, Joseph A, Yogananda, Chandan Ganesh Bangalore, Pinho, Marco C, Reddy, Divya, Holcomb, James, Wagner, Benjamin C, Ellingson, Benjamin M, Cloughesy, Timothy F, Raymond, Catalina, Oughourlian, Talia, Hagiwara, Akifumi, Wang, Chencai, To, Minh-Son, Bhardwaj, Sargam, Chong, Chee, Agzarian, Marc, Falcão, Alexandre Xavier, Martins, Samuel B, Teixeira, Bernardo C A, Sprenger, Flávia, Menotti, David, Lucio, Diego R, LaMontagne, Pamela, Marcus, Daniel, Wiestler, Benedikt, Kofler, Florian, Ezhov, Ivan, Metz, Marie, Jain, Rajan, Lee, Matthew, Lui, Yvonne W, McKinley, Richard, Slotboom, Johannes, Radojewski, Piotr, Meier, Raphael, Wiest, Roland, Murcia, Derrick, Fu, Eric, Haas, Rourke, Thompson, John, Ormond, David Ryan, Badve, Chaitra, Sloan, Andrew E, Vadmal, Vachan, Waite, Kristin, Colen, Rivka R, Pei, Linmin, Ak, Murat, Srinivasan, Ashok, Bapuraj, J Rajiv, Rao, Arvind, Wang, Nicholas, Yoshiaki, Ota, Moritani, Toshio, Turk, Sevcan, Lee, Joonsang, Prabhudesai, Snehal, Morón, Fanny, Mandel, Jacob, Kamnitsas, Konstantinos, Glocker, Ben, Dixon, Luke V M, Williams, Matthew, Zampakis, Peter, Panagiotopoulos, Vasileios, Tsiganos, Panagiotis, Alexiou, Sotiris, Haliassos, Ilias, Zacharaki, Evangelia I, Moustakas, Konstantinos, Kalogeropoulou, Christina, Kardamakis, Dimitrios M, Choi, Yoon Seong, Lee, Seung-Koo, Chang, Jong Hee, Ahn, Sung Soo, Luo, Bing, Poisson, Laila, Wen, Ning, Tiwari, Pallavi, Verma, Ruchika, Bareja, Rohan, Yadav, Ipsa, Chen, Jonathan, Kumar, Neeraj, Smits, Marion, van der Voort, Sebastian R, Alafandi, Ahmed, Incekara, Fatih, Wijnenga, Maarten MJ, Kapsas, Georgios, Gahrmann, Renske, Schouten, Joost W, Dubbink, Hendrikus J, Vincent, Arnaud JPE, Bent, Martin J van den, French, Pim J, Klein, Stefan, Yuan, Yading, Sharma, Sonam, Tseng, Tzu-Chi, Adabi, Saba, Niclou, Simone P, Keunen, Olivier, Hau, Ann-Christin, Vallières, Martin, Fortin, David, Lepage, Martin, Landman, Bennett, Ramadass, Karthik, Xu, Kaiwen, Chotai, Silky, Chambless, Lola B, Mistry, Akshitkumar, Thompson, Reid C, Gusev, Yuriy, Bhuvaneshwar, Krithika, Sayah, Anousheh, Bencheqroun, Camelia, Belouali, Anas, Madhavan, Subha, Booth, Thomas C, Chelliah, Alysha, Modat, Marc, Shuaib, Haris, Dragos, Carmen, Abayazeed, Aly, Kolodziej, Kenneth, Hill, Michael, Abbassy, Ahmed, Gamal, Shady, Mekhaimar, Mahmoud, Qayati, Mohamed, Reyes, Mauricio, Park, Ji Eun, Yun, Jihye, Kim, Ho Sung, Mahajan, Abhishek, Muzi, Mark, Benson, Sean, Beets-Tan, Regina G H, Teuwen, Jonas, Herrera-Trujillo, Alejandro, Trujillo, Maria, Escobar, William, Abello, Ana, Bernal, Jose, Gómez, Jhon, Choi, Joseph, Baek, Stephen, Kim, Yusung, Ismael, Heba, Allen, Bryan, Buatti, John M, Kotrotsou, Aikaterini, Li, Hongwei, Weiss, Tobias, Weller, Michael, Bink, Andrea, Pouymayou, Bertrand, Shaykh, Hassan F, Saltz, Joel, Prasanna, Prateek, Shrestha, Sampurna, Mani, Kartik M, Payne, David, Kurc, Tahsin, Pelaez, Enrique, Franco-Maldonado, Heydy, Loayza, Francis, Quevedo, Sebastian, Guevara, Pamela, Torche, Esteban, Mendoza, Cristobal, Vera, Franco, Ríos, Elvis, López, Eduardo, Velastin, Sergio A, Ogbole, Godwin, Oyekunle, Dotun, Odafe-Oyibotha, Olubunmi, Osobu, Babatunde, Shu'aibu, Mustapha, Dorcas, Adeleye, Soneye, Mayowa, Dako, Farouk, Simpson, Amber L, Hamghalam, Mohammad, Peoples, Jacob J, Hu, Ricky, Tran, Anh, Cutler, Danielle, Moraes, Fabio Y, Boss, Michael A, Gimpel, James, Veettil, Deepak Kattil, Schmidt, Kendall, Bialecki, Brian, Marella, Sailaja, Price, Cynthia, Cimino, Lisa, Apgar, Charles, Shah, Prashant, Menze, Bjoern, Barnholtz-Sloan, Jill S, Martin, Jason, and Bakas, Spyridon

Subjects
Computer Science - Machine Learning, Electrical Engineering and Systems Science - Image and Video Processing
Abstract

Although machine learning (ML) has shown promise in numerous domains, there are concerns about generalizability to out-of-sample data. This is currently addressed by centrally sharing ample, and importantly diverse, data from multiple sites. However, such centralization is challenging to scale (or even not feasible) due to various limitations. Federated ML (FL) provides an alternative to train accurate and generalizable ML models, by only sharing numerical model updates. Here we present findings from the largest FL study to-date, involving data from 71 healthcare institutions across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, utilizing the largest dataset of such patients ever used in the literature (25,256 MRI scans from 6,314 patients). We demonstrate a 33% improvement over a publicly trained model to delineate the surgically targetable tumor, and 23% improvement over the tumor's entire extent. We anticipate our study to: 1) enable more studies in healthcare informed by large and diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further quantitative analyses for glioblastoma via performance optimization of our consensus model for eventual public release, and 3) demonstrate the effectiveness of FL at such scale and task complexity as a paradigm shift for multi-site collaborations, alleviating the need for data sharing., Comment: federated learning, deep learning, convolutional neural network, segmentation, brain tumor, glioma, glioblastoma, FeTS, BraTS

Published
2022
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2. A Smartphone Client-Server Teleradiology System for Primary Diagnosis of Acute Stroke

Author

Mitchell, J Ross, Sharma, Pranshu, Modi, Jayesh, Simpson, Mark, Thomas, Monroe, Hill, Michael D, and Goyal, Mayank

Subjects
Computer applications to medicine. Medical informatics, R858-859.7, Public aspects of medicine, RA1-1270
Abstract

BackgroundRecent advances in the treatment of acute ischemic stroke have made rapid acquisition, visualization, and interpretation of images a key factor for positive patient outcomes. We have developed a new teleradiology system based on a client-server architecture that enables rapid access to interactive advanced 2-D and 3-D visualization on a current generation smartphone device (Apple iPhone or iPod Touch, or an Android phone) without requiring patient image data to be stored on the device. Instead, a server loads and renders the patient images, then transmits a rendered frame to the remote device. ObjectiveOur objective was to determine if a new smartphone client-server teleradiology system is capable of providing accuracies and interpretation times sufficient for diagnosis of acute stroke. MethodsThis was a retrospective study. We obtained 120 recent consecutive noncontrast computed tomography (NCCT) brain scans and 70 computed tomography angiogram (CTA) head scans from the Calgary Stroke Program database. Scans were read by two neuroradiologists, one on a medical diagnostic workstation and an iPod or iPhone (hereafter referred to as an iOS device) and the other only on an iOS device. NCCT brain scans were evaluated for early signs of infarction, which includes early parenchymal ischemic changes and dense vessel sign, and to exclude acute intraparenchymal hemorrhage and stroke mimics. CTA brain scans were evaluated for any intracranial vessel occlusion. The interpretations made on an iOS device were compared with those made at a workstation. The total interpretation times were recorded for both platforms. Interrater agreement was assessed. True positives, true negatives, false positives, and false negatives were obtained, and sensitivity, specificity, and accuracy of detecting the abnormalities on the iOS device were computed. ResultsThe sensitivity, specificity, and accuracy of detecting intraparenchymal hemorrhage were 100% using the iOS device with a perfect interrater agreement (kappa = 1). The sensitivity, specificity, and accuracy of detecting acute parenchymal ischemic change were 94.1%, 100%, and 98.09% respectively for reader 1 and 97.05%, 100%, and 99.04% for reader 2 with nearly perfect interrater agreement (kappa = .8). The sensitivity, specificity, and accuracy of detecting dense vessel sign were 100%, 95.4%, and 96.19% respectively for reader 1 and 72.2%, 100%, and 95.23% for reader 2 using the iOS device with a good interrater agreement (kappa = .69). The sensitivity, specificity, and accuracy of detecting vessel occlusion on CT angiography scans were 94.4%, 100%, and 98.46% respectively for both readers using the iOS device, with perfect interrater agreement (kappa = 1). No significant difference (P < .05) was noted in the interpretation time between the workstation and iOS device. ConclusionThe smartphone client-server teleradiology system appears promising and may have the potential to allow urgent management decisions in acute stroke. However, this study was retrospective, involved relatively few patient studies, and only two readers. Generalizing conclusions about its clinical utility, especially in other diagnostic use cases, should not be made until additional studies are performed.

Published
2011
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3. Author Correction: Federated learning enables big data for rare cancer boundary detection

Author

Pati, Sarthak, Baid, Ujjwal, Edwards, Brandon, Sheller, Micah, Wang, Shih-Han, Reina, G. Anthony, Foley, Patrick, Gruzdev, Alexey, Karkada, Deepthi, Davatzikos, Christos, Sako, Chiharu, Ghodasara, Satyam, Bilello, Michel, Mohan, Suyash, Vollmuth, Philipp, Brugnara, Gianluca, Preetha, Chandrakanth J., Sahm, Felix, Maier-Hein, Klaus, Zenk, Maximilian, Bendszus, Martin, Wick, Wolfgang, Calabrese, Evan, Rudie, Jeffrey, Villanueva-Meyer, Javier, Cha, Soonmee, Ingalhalikar, Madhura, Jadhav, Manali, Pandey, Umang, Saini, Jitender, Garrett, John, Larson, Matthew, Jeraj, Robert, Currie, Stuart, Frood, Russell, Fatania, Kavi, Huang, Raymond Y., Chang, Ken, Balaña, Carmen, Capellades, Jaume, Puig, Josep, Trenkler, Johannes, Pichler, Josef, Necker, Georg, Haunschmidt, Andreas, Meckel, Stephan, Shukla, Gaurav, Liem, Spencer, Alexander, Gregory S., Lombardo, Joseph, Palmer, Joshua D., Flanders, Adam E., Dicker, Adam P., Sair, Haris I., Jones, Craig K., Venkataraman, Archana, Jiang, Meirui, So, Tiffany Y., Chen, Cheng, Heng, Pheng Ann, Dou, Qi, Kozubek, Michal, Lux, Filip, Michálek, Jan, Matula, Petr, Keřkovský, Miloš, Kopřivová, Tereza, Dostál, Marek, Vybíhal, Václav, Vogelbaum, Michael A., Mitchell, J. Ross, Farinhas, Joaquim, Maldjian, Joseph A., Yogananda, Chandan Ganesh Bangalore, Pinho, Marco C., Reddy, Divya, Holcomb, James, Wagner, Benjamin C., Ellingson, Benjamin M., Cloughesy, Timothy F., Raymond, Catalina, Oughourlian, Talia, Hagiwara, Akifumi, Wang, Chencai, To, Minh-Son, Bhardwaj, Sargam, Chong, Chee, Agzarian, Marc, Falcão, Alexandre Xavier, Martins, Samuel B., Teixeira, Bernardo C. A., Sprenger, Flávia, Menotti, David, Lucio, Diego R., LaMontagne, Pamela, Marcus, Daniel, Wiestler, Benedikt, Kofler, Florian, Ezhov, Ivan, Metz, Marie, Jain, Rajan, Lee, Matthew, Lui, Yvonne W., McKinley, Richard, Slotboom, Johannes, Radojewski, Piotr, Meier, Raphael, Wiest, Roland, Murcia, Derrick, Fu, Eric, Haas, Rourke, Thompson, John, Ormond, David Ryan, Badve, Chaitra, Sloan, Andrew E., Vadmal, Vachan, Waite, Kristin, Colen, Rivka R., Pei, Linmin, Ak, Murat, Srinivasan, Ashok, Bapuraj, J. Rajiv, Rao, Arvind, Wang, Nicholas, Yoshiaki, Ota, Moritani, Toshio, Turk, Sevcan, Lee, Joonsang, Prabhudesai, Snehal, Morón, Fanny, Mandel, Jacob, Kamnitsas, Konstantinos, Glocker, Ben, Dixon, Luke V. M., Williams, Matthew, Zampakis, Peter, Panagiotopoulos, Vasileios, Tsiganos, Panagiotis, Alexiou, Sotiris, Haliassos, Ilias, Zacharaki, Evangelia I., Moustakas, Konstantinos, Kalogeropoulou, Christina, Kardamakis, Dimitrios M., Choi, Yoon Seong, Lee, Seung-Koo, Chang, Jong Hee, Ahn, Sung Soo, Luo, Bing, Poisson, Laila, Wen, Ning, Tiwari, Pallavi, Verma, Ruchika, Bareja, Rohan, Yadav, Ipsa, Chen, Jonathan, Kumar, Neeraj, Smits, Marion, van der Voort, Sebastian R., Alafandi, Ahmed, Incekara, Fatih, Wijnenga, Maarten M. J., Kapsas, Georgios, Gahrmann, Renske, Schouten, Joost W., Dubbink, Hendrikus J., Vincent, Arnaud J. P. E., van den Bent, Martin J., French, Pim J., Klein, Stefan, Yuan, Yading, Sharma, Sonam, Tseng, Tzu-Chi, Adabi, Saba, Niclou, Simone P., Keunen, Olivier, Hau, Ann-Christin, Vallières, Martin, Fortin, David, Lepage, Martin, Landman, Bennett, Ramadass, Karthik, Xu, Kaiwen, Chotai, Silky, Chambless, Lola B., Mistry, Akshitkumar, Thompson, Reid C., Gusev, Yuriy, Bhuvaneshwar, Krithika, Sayah, Anousheh, Bencheqroun, Camelia, Belouali, Anas, Madhavan, Subha, Booth, Thomas C., Chelliah, Alysha, Modat, Marc, Shuaib, Haris, Dragos, Carmen, Abayazeed, Aly, Kolodziej, Kenneth, Hill, Michael, Abbassy, Ahmed, Gamal, Shady, Mekhaimar, Mahmoud, Qayati, Mohamed, Reyes, Mauricio, Park, Ji Eun, Yun, Jihye, Kim, Ho Sung, Mahajan, Abhishek, Muzi, Mark, Benson, Sean, Beets-Tan, Regina G. H., Teuwen, Jonas, Herrera-Trujillo, Alejandro, Trujillo, Maria, Escobar, William, Abello, Ana, Bernal, Jose, Gómez, Jhon, Choi, Joseph, Baek, Stephen, Kim, Yusung, Ismael, Heba, Allen, Bryan, Buatti, John M., Kotrotsou, Aikaterini, Li, Hongwei, Weiss, Tobias, Weller, Michael, Bink, Andrea, Pouymayou, Bertrand, Shaykh, Hassan F., Saltz, Joel, Prasanna, Prateek, Shrestha, Sampurna, Mani, Kartik M., Payne, David, Kurc, Tahsin, Pelaez, Enrique, Franco-Maldonado, Heydy, Loayza, Francis, Quevedo, Sebastian, Guevara, Pamela, Torche, Esteban, Mendoza, Cristobal, Vera, Franco, Ríos, Elvis, López, Eduardo, Velastin, Sergio A., Ogbole, Godwin, Soneye, Mayowa, Oyekunle, Dotun, Odafe-Oyibotha, Olubunmi, Osobu, Babatunde, Shu’aibu, Mustapha, Dorcas, Adeleye, Dako, Farouk, Simpson, Amber L., Hamghalam, Mohammad, Peoples, Jacob J., Hu, Ricky, Tran, Anh, Cutler, Danielle, Moraes, Fabio Y., Boss, Michael A., Gimpel, James, Veettil, Deepak Kattil, Schmidt, Kendall, Bialecki, Brian, Marella, Sailaja, Price, Cynthia, Cimino, Lisa, Apgar, Charles, Shah, Prashant, Menze, Bjoern, Barnholtz-Sloan, Jill S., Martin, Jason, and Bakas, Spyridon

Published
2023
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4. Robust Automatic Whole Brain Extraction on Magnetic Resonance Imaging of Brain Tumor Patients using Dense-Vnet

Author

Ranjbar, Sara, Singleton, Kyle W., Curtin, Lee, Rickertsen, Cassandra R., Paulson, Lisa E., Hu, Leland S., Mitchell, J. Ross, and Swanson, Kristin R.

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Quantitative Biology - Quantitative Methods
Abstract

Whole brain extraction, also known as skull stripping, is a process in neuroimaging in which non-brain tissue such as skull, eyeballs, skin, etc. are removed from neuroimages. Skull striping is a preliminary step in presurgical planning, cortical reconstruction, and automatic tumor segmentation. Despite a plethora of skull stripping approaches in the literature, few are sufficiently accurate for processing pathology-presenting MRIs, especially MRIs with brain tumors. In this work we propose a deep learning approach for skull striping common MRI sequences in oncology such as T1-weighted with gadolinium contrast (T1Gd) and T2-weighted fluid attenuated inversion recovery (FLAIR) in patients with brain tumors. We automatically created gray matter, white matter, and CSF probability masks using SPM12 software and merged the masks into one for a final whole-brain mask for model training. Dice agreement, sensitivity, and specificity of the model (referred herein as DeepBrain) was tested against manual brain masks. To assess data efficiency, we retrained our models using progressively fewer training data examples and calculated average dice scores on the test set for the models trained in each round. Further, we tested our model against MRI of healthy brains from the LBP40A dataset. Overall, DeepBrain yielded an average dice score of 94.5%, sensitivity of 96.4%, and specificity of 98.5% on brain tumor data. For healthy brains, model performance improved to a dice score of 96.2%, sensitivity of 96.6% and specificity of 99.2%. The data efficiency experiment showed that, for this specific task, comparable levels of accuracy could have been achieved with as few as 50 training samples. In conclusion, this study demonstrated that a deep learning model trained on minimally processed automatically-generated labels can generate more accurate brain masks on MRI of brain tumor patients within seconds.

Published
2020

7. Canadian Association of Radiologists White Paper on Artificial Intelligence in Radiology

Author

O'Connell, Timothy, Babyn, Paul, Koff, David, Ferguson, Darren, Derkatch, Sheldon, Bilbily, Alexander, Shabana, Wael, Tang, An, Tam, Roger, Cadrin-Chênevert, Alexandre, Guest, Will, Chong, Jaron, Barfett, Joseph, Chepelev, Leonid, Cairns, Robyn, Mitchell, J. Ross, Cicero, Mark D., Poudrette, Manuel Gaudreau, Jaremko, Jacob L., Reinhold, Caroline, Gallix, Benoit, Gray, Bruce, and Geis, Raym

Published
2018
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9. Federated learning enables big data for rare cancer boundary detection

Author

Pati, Sarthak, primary, Baid, Ujjwal, additional, Edwards, Brandon, additional, Sheller, Micah, additional, Wang, Shih-Han, additional, Reina, G. Anthony, additional, Foley, Patrick, additional, Gruzdev, Alexey, additional, Karkada, Deepthi, additional, Davatzikos, Christos, additional, Sako, Chiharu, additional, Ghodasara, Satyam, additional, Bilello, Michel, additional, Mohan, Suyash, additional, Vollmuth, Philipp, additional, Brugnara, Gianluca, additional, Preetha, Chandrakanth J., additional, Sahm, Felix, additional, Maier-Hein, Klaus, additional, Zenk, Maximilian, additional, Bendszus, Martin, additional, Wick, Wolfgang, additional, Calabrese, Evan, additional, Rudie, Jeffrey, additional, Villanueva-Meyer, Javier, additional, Cha, Soonmee, additional, Ingalhalikar, Madhura, additional, Jadhav, Manali, additional, Pandey, Umang, additional, Saini, Jitender, additional, Garrett, John, additional, Larson, Matthew, additional, Jeraj, Robert, additional, Currie, Stuart, additional, Frood, Russell, additional, Fatania, Kavi, additional, Huang, Raymond Y., additional, Chang, Ken, additional, Balaña, Carmen, additional, Capellades, Jaume, additional, Puig, Josep, additional, Trenkler, Johannes, additional, Pichler, Josef, additional, Necker, Georg, additional, Haunschmidt, Andreas, additional, Meckel, Stephan, additional, Shukla, Gaurav, additional, Liem, Spencer, additional, Alexander, Gregory S., additional, Lombardo, Joseph, additional, Palmer, Joshua D., additional, Flanders, Adam E., additional, Dicker, Adam P., additional, Sair, Haris I., additional, Jones, Craig K., additional, Venkataraman, Archana, additional, Jiang, Meirui, additional, So, Tiffany Y., additional, Chen, Cheng, additional, Heng, Pheng Ann, additional, Dou, Qi, additional, Kozubek, Michal, additional, Lux, Filip, additional, Michálek, Jan, additional, Matula, Petr, additional, Keřkovský, Miloš, additional, Kopřivová, Tereza, additional, Dostál, Marek, additional, Vybíhal, Václav, additional, Vogelbaum, Michael A., additional, Mitchell, J. Ross, additional, Farinhas, Joaquim, additional, Maldjian, Joseph A., additional, Yogananda, Chandan Ganesh Bangalore, additional, Pinho, Marco C., additional, Reddy, Divya, additional, Holcomb, James, additional, Wagner, Benjamin C., additional, Ellingson, Benjamin M., additional, Cloughesy, Timothy F., additional, Raymond, Catalina, additional, Oughourlian, Talia, additional, Hagiwara, Akifumi, additional, Wang, Chencai, additional, To, Minh-Son, additional, Bhardwaj, Sargam, additional, Chong, Chee, additional, Agzarian, Marc, additional, Falcão, Alexandre Xavier, additional, Martins, Samuel B., additional, Teixeira, Bernardo C. A., additional, Sprenger, Flávia, additional, Menotti, David, additional, Lucio, Diego R., additional, LaMontagne, Pamela, additional, Marcus, Daniel, additional, Wiestler, Benedikt, additional, Kofler, Florian, additional, Ezhov, Ivan, additional, Metz, Marie, additional, Jain, Rajan, additional, Lee, Matthew, additional, Lui, Yvonne W., additional, McKinley, Richard, additional, Slotboom, Johannes, additional, Radojewski, Piotr, additional, Meier, Raphael, additional, Wiest, Roland, additional, Murcia, Derrick, additional, Fu, Eric, additional, Haas, Rourke, additional, Thompson, John, additional, Ormond, David Ryan, additional, Badve, Chaitra, additional, Sloan, Andrew E., additional, Vadmal, Vachan, additional, Waite, Kristin, additional, Colen, Rivka R., additional, Pei, Linmin, additional, Ak, Murat, additional, Srinivasan, Ashok, additional, Bapuraj, J. Rajiv, additional, Rao, Arvind, additional, Wang, Nicholas, additional, Yoshiaki, Ota, additional, Moritani, Toshio, additional, Turk, Sevcan, additional, Lee, Joonsang, additional, Prabhudesai, Snehal, additional, Morón, Fanny, additional, Mandel, Jacob, additional, Kamnitsas, Konstantinos, additional, Glocker, Ben, additional, Dixon, Luke V. M., additional, Williams, Matthew, additional, Zampakis, Peter, additional, Panagiotopoulos, Vasileios, additional, Tsiganos, Panagiotis, additional, Alexiou, Sotiris, additional, Haliassos, Ilias, additional, Zacharaki, Evangelia I., additional, Moustakas, Konstantinos, additional, Kalogeropoulou, Christina, additional, Kardamakis, Dimitrios M., additional, Choi, Yoon Seong, additional, Lee, Seung-Koo, additional, Chang, Jong Hee, additional, Ahn, Sung Soo, additional, Luo, Bing, additional, Poisson, Laila, additional, Wen, Ning, additional, Tiwari, Pallavi, additional, Verma, Ruchika, additional, Bareja, Rohan, additional, Yadav, Ipsa, additional, Chen, Jonathan, additional, Kumar, Neeraj, additional, Smits, Marion, additional, van der Voort, Sebastian R., additional, Alafandi, Ahmed, additional, Incekara, Fatih, additional, Wijnenga, Maarten M. J., additional, Kapsas, Georgios, additional, Gahrmann, Renske, additional, Schouten, Joost W., additional, Dubbink, Hendrikus J., additional, Vincent, Arnaud J. P. E., additional, van den Bent, Martin J., additional, French, Pim J., additional, Klein, Stefan, additional, Yuan, Yading, additional, Sharma, Sonam, additional, Tseng, Tzu-Chi, additional, Adabi, Saba, additional, Niclou, Simone P., additional, Keunen, Olivier, additional, Hau, Ann-Christin, additional, Vallières, Martin, additional, Fortin, David, additional, Lepage, Martin, additional, Landman, Bennett, additional, Ramadass, Karthik, additional, Xu, Kaiwen, additional, Chotai, Silky, additional, Chambless, Lola B., additional, Mistry, Akshitkumar, additional, Thompson, Reid C., additional, Gusev, Yuriy, additional, Bhuvaneshwar, Krithika, additional, Sayah, Anousheh, additional, Bencheqroun, Camelia, additional, Belouali, Anas, additional, Madhavan, Subha, additional, Booth, Thomas C., additional, Chelliah, Alysha, additional, Modat, Marc, additional, Shuaib, Haris, additional, Dragos, Carmen, additional, Abayazeed, Aly, additional, Kolodziej, Kenneth, additional, Hill, Michael, additional, Abbassy, Ahmed, additional, Gamal, Shady, additional, Mekhaimar, Mahmoud, additional, Qayati, Mohamed, additional, Reyes, Mauricio, additional, Park, Ji Eun, additional, Yun, Jihye, additional, Kim, Ho Sung, additional, Mahajan, Abhishek, additional, Muzi, Mark, additional, Benson, Sean, additional, Beets-Tan, Regina G. H., additional, Teuwen, Jonas, additional, Herrera-Trujillo, Alejandro, additional, Trujillo, Maria, additional, Escobar, William, additional, Abello, Ana, additional, Bernal, Jose, additional, Gómez, Jhon, additional, Choi, Joseph, additional, Baek, Stephen, additional, Kim, Yusung, additional, Ismael, Heba, additional, Allen, Bryan, additional, Buatti, John M., additional, Kotrotsou, Aikaterini, additional, Li, Hongwei, additional, Weiss, Tobias, additional, Weller, Michael, additional, Bink, Andrea, additional, Pouymayou, Bertrand, additional, Shaykh, Hassan F., additional, Saltz, Joel, additional, Prasanna, Prateek, additional, Shrestha, Sampurna, additional, Mani, Kartik M., additional, Payne, David, additional, Kurc, Tahsin, additional, Pelaez, Enrique, additional, Franco-Maldonado, Heydy, additional, Loayza, Francis, additional, Quevedo, Sebastian, additional, Guevara, Pamela, additional, Torche, Esteban, additional, Mendoza, Cristobal, additional, Vera, Franco, additional, Ríos, Elvis, additional, López, Eduardo, additional, Velastin, Sergio A., additional, Ogbole, Godwin, additional, Soneye, Mayowa, additional, Oyekunle, Dotun, additional, Odafe-Oyibotha, Olubunmi, additional, Osobu, Babatunde, additional, Shu’aibu, Mustapha, additional, Dorcas, Adeleye, additional, Dako, Farouk, additional, Simpson, Amber L., additional, Hamghalam, Mohammad, additional, Peoples, Jacob J., additional, Hu, Ricky, additional, Tran, Anh, additional, Cutler, Danielle, additional, Moraes, Fabio Y., additional, Boss, Michael A., additional, Gimpel, James, additional, Veettil, Deepak Kattil, additional, Schmidt, Kendall, additional, Bialecki, Brian, additional, Marella, Sailaja, additional, Price, Cynthia, additional, Cimino, Lisa, additional, Apgar, Charles, additional, Shah, Prashant, additional, Menze, Bjoern, additional, Barnholtz-Sloan, Jill S., additional, Martin, Jason, additional, and Bakas, Spyridon, additional

Published
2022
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12. A Novel MRI Texture Analysis of Demyelination and Inflammation in Relapsing-Remitting Experimental Allergic Encephalomyelitis

Author

Zhang, Yunyan, Wells, Jennifer, Buist, Richard, Peeling, James, Yong, V. Wee, Mitchell, J. Ross, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Larsen, Rasmus, editor, Nielsen, Mads, editor, and Sporring, Jon, editor

Published
2006
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17. Considerations for the Use of Handheld Image Viewers: The Report of AAPM Task Group 260

Author

Badano, Aldo, primary, Bevins, Nicholas, additional, Flynn, Michael, additional, Gurel, Ogan, additional, Hasegawa, Mikio, additional, Heckel, Ing. Juergen, additional, Kakuda, Koichi, additional, Lamb, Jessica, additional, Langer, Steve, additional, Masia, Andy, additional, Mitchell, J. Ross, additional, Nagy, Balazs, additional, Penczek, John, additional, Van Hoey, Gert, additional, Peterson, Kyle, additional, Redmond, George, additional, Srinivasan, Girish, additional, Steven, Peter, additional, Toomey, Rachel, additional, and Walz-Flannigan, Alisa, additional

Published
2018
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33. Sex differences in seizure at presentation in glioma population

Author

Ranjbar, Sara, primary, Anderies, Barrett J, additional, Singleton, Kyle W, additional, Johnston, Sandra K, additional, Rickertsen, Cassandra R, additional, Sharma, Akanshka, additional, Porter, Alyx B, additional, Mrugala, Maciej M, additional, Hu, Leland S, additional, Mitchell, J Ross, additional, Rubin, Joshua B, additional, and Swanson, Kristin R, additional

Published
2019
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34. Integration of machine learning and mechanistic models accurately predicts variation in cell density of glioblastoma using multiparametric MRI

Author

Gaw, Nathan, primary, Hawkins-Daarud, Andrea, additional, Hu, Leland S., additional, Yoon, Hyunsoo, additional, Wang, Lujia, additional, Xu, Yanzhe, additional, Jackson, Pamela R., additional, Singleton, Kyle W., additional, Baxter, Leslie C., additional, Eschbacher, Jennifer, additional, Gonzales, Ashlyn, additional, Nespodzany, Ashley, additional, Smith, Kris, additional, Nakaji, Peter, additional, Mitchell, J. Ross, additional, Wu, Teresa, additional, Swanson, Kristin R., additional, and Li, Jing, additional

Published
2019
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35. Distributed vector processing of a new local multiscale Fourier transform for medical imaging applications

Author

Brown, Robert A., Zhu, Hongmei, and Mitchell, J. Ross

Subjects
Wavelet transforms -- Research, Wavelet transforms -- Technology application, Fourier transformations -- Technology application, Fourier transformations -- Research, Medical imaging equipment -- Research, Technology application, Business, Electronics, Electronics and electrical industries, Health care industry
Abstract

The recently developed S-transform (ST) combines features of the Fourier and Wavelet transforms; it reveals frequency variation over both space and time. It is a potentially powerful tool that can be applied to medical image processing including texture analysis and noise filtering. However, calculation of the ST is computationally intensive, making conventional implementations too slow for many medical applications. This problem was addressed by combining parallel and vector computations to provide a 25-fold reduction in computation time. This approach could help accelerate many medical image processing algorithms. Index Terms--Biomedical image processing, discrete Fourier transforms, distributed computing, vector processing.

Published
2005

36. Validity of the Apple iPhone®/iPod Touch® as an Accelerometer-Based Physical Activity Monitor: A Proof-of-Concept Study.

Author

Nolan, Meaghan, Mitchell, J. Ross, and Doyle-Baker, Patricia K.

Subjects
ACCELEROMETERS, IPHONE (Smartphone), IPOD (Digital music player), PHYSICAL activity, CALORIC expenditure, ACQUISITION of data, LEGAL compliance
Abstract

Background: The popularity of smartphones has led researchers to ask if they can replace traditional tools for assessing free-living physical activity. Our purpose was to establish proof-of-concept that a smartphone could record acceleration during physical activity, and those data could be modeled to predict activity type (walking or running), speed (km⋅h-1), and energy expenditure (METs). Methods: An application to record and e-mail accelerations was developed for the Apple iPhone®/iPod Touch®. Twenty-five healthy adults performed treadmill walking (4.0 km⋅h-1 to 7.2 km⋅h-1) and running (8.1 km⋅h-1 to 11.3 km⋅h-1) wearing the device. Criterion energy expenditure measurements were collected via metabolic cart. Results: Activity type was classified with 99% accuracy. Speed was predicted with a bias of 0.02 km⋅h-1 (SEE: 0.57 km⋅h-1) for walking, -0.03 km⋅h-1 (SEE: 1.02 km⋅h-1) for running. Energy expenditure was predicted with a bias of 0.35 METs (SEE: 0.75 METs) for walking, -0.43 METs (SEE: 1.24 METs) for running. Conclusion: Our results suggest that an iPhone/iPod Touch can predict aspects of locomotion with accuracy similar to other accelerometer-based tools. Future studies may leverage this and the additional features of smartphones to improve data collection and compliance. [ABSTRACT FROM AUTHOR]

Published
2014
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37. An algorithm for noise correction of dual-energy computed tomography material density images

Author

Alvin C. Silva, Christian Jacob, Amy K. Hara, William Pavlicek, Rafael Simon Maia, and Mitchell J. Ross

Subjects
Anisotropic diffusion, Image quality, Noise reduction, Biomedical Engineering, Contrast Media, Health Informatics, Signal-To-Noise Ratio, Image Processing, Computer-Assisted, Image noise, Humans, Radiology, Nuclear Medicine and imaging, Mathematics, Pixel, Resolution (electron density), Dual-Energy Computed Tomography, General Medicine, Magnetic Resonance Imaging, Computer Graphics and Computer-Aided Design, Computer Science Applications, Noise, Computer Science::Computer Vision and Pattern Recognition, Anisotropy, Surgery, Computer Vision and Pattern Recognition, Tomography, X-Ray Computed, Algorithm, Algorithms
Abstract

Dual-energy computed tomography (DECT) images can undergo a two-material decomposition process which results in two images containing material density information. Material density images obtained by that process result in images with increased pixel noise. Noise reduction in those images is desirable in order to improve image quality. A noise reduction algorithm for material density images was developed and tested. A three-level wavelet approach combined with the application of an anisotropic diffusion filter was used. During each level, the resulting noise maps are further processed, until the original resolution is reached and the final noise maps obtained. Our method works in image space and, therefore, can be applied to any type of material density images obtained from any DECT vendor. A quantitative evaluation of the noise-reduced images using the signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR) and 2D noise power spectrum was done to quantify the improvements. The noise reduction algorithm was applied to a set of images resulting in images with higher SNR and CNR than the raw density images obtained by the decomposition process. The average improvement in terms of SNR gain was about 49 % while CNR gain was about 52 %. The difference between the raw and filtered regions of interest mean values was far from reaching statistical significance (minimum $$p> 0.89$$ , average $$p> 0.97$$ ). We have demonstrated through a series of quantitative analyses that our novel noise reduction algorithm improves the image quality of DECT material density images.

Published
2014

39. Abstract A08: Histologic evidence for a bio-mathematical model of glioblastoma invasion

Author

Hawkins-Daarud, Andrea, primary, DeGirolamo, Lauren, additional, Jacobs, Joshua, additional, Clark-Swanson, Kamala, additional, Eschbacher, Jennifer M., additional, Smith, Kris A., additional, Nakaji, Peter, additional, Baxter, Leslie C., additional, Karis, John P., additional, Wu, Teresa, additional, Mitchell, J. Ross, additional, Li, Jing, additional, Hu, Leland, additional, and Swanson, Kristin R., additional

Published
2017
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40. Angiographic variables predict increased riskfor adverse ischemic events after coronarystenting with glycoprotein IIb/IIIa inhibition

Author

Stephen G. Ellis, Howard C. Herrmann, Jack L. Martin, Eric J. Topol, Laura A. Demopoulos, David J. Moliterno, Peter M. DiBattiste, James C. Blankenship, Ziyad M.B. Ghazzal, Mitchell J. Ross, and Jennifer White

Subjects
medicine.medical_specialty, business.industry, medicine.medical_treatment, Stent, Tirofiban, medicine.disease, Surgery, law.invention, Clinical trial, Randomized controlled trial, law, Angioplasty, Internal medicine, Abciximab, Cardiology, Medicine, Myocardial infarction, Glycoprotein IIb/IIIa, business, Cardiology and Cardiovascular Medicine, medicine.drug
Abstract

OBJECTIVES We sought to assess whether pre-procedural angiographic characteristics are associated with adverse clinical outcomes after coronary stenting with glycoprotein IIb/IIIa inhibition. BACKGROUND Ischemic complications after balloon angioplasty are associated with pre- and post-procedural angiographic variables. However, in the current era of stenting with IIb/IIIa inhibition, it is unknown whether angiographic features assessed before intervention confer an increased risk of adverse procedural and subsequent clinical outcomes. METHODS In the Do Tirofiban and ReoPro Give Similar Efficacy Outcomes? Trial (TARGET), 4,809 patients undergoing planned stenting were randomized to tirofiban or abciximab. Baseline demographic, clinical, and angiographic variables were obtained. Clinical end points were recorded at 30 days and six months. The relationship between angiographic variables and adverse clinical outcomes was assessed. RESULTS Patients with the combination of thrombus, lesion eccentricity, and lesion length >20 mm had a 21.4% composite incidence of death, myocardial infarction, or urgent target vessel revascularization (TVR) at 30 days, compared with 4.2% in those patients without these high-risk features (hazard ratio [HR] 3.24, p 20 mm (HR 1.89, p 20 mm are at high risk for ischemic outcomes after coronary stenting, despite IIb/IIIa inhibition. Further research into novel anti-thrombotic therapies or procedural strategies is necessary for these patients.

Published
2003
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41. Radiogenomics to characterize regional genetic heterogeneity in glioblastoma

Author

Hu, Leland S., primary, Ning, Shuluo, additional, Eschbacher, Jennifer M., additional, Baxter, Leslie C., additional, Gaw, Nathan, additional, Ranjbar, Sara, additional, Plasencia, Jonathan, additional, Dueck, Amylou C., additional, Peng, Sen, additional, Smith, Kris A., additional, Nakaji, Peter, additional, Karis, John P., additional, Quarles, C. Chad, additional, Wu, Teresa, additional, Loftus, Joseph C., additional, Jenkins, Robert B., additional, Sicotte, Hugues, additional, Kollmeyer, Thomas M., additional, O'Neill, Brian P., additional, Elmquist, William, additional, Hoxworth, Joseph M., additional, Frakes, David, additional, Sarkaria, Jann, additional, Swanson, Kristin R., additional, Tran, Nhan L., additional, Li, Jing, additional, and Mitchell, J. Ross, additional

Published
2016
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42. Multi-Parametric MRI and Texture Analysis to Visualize Spatial Histologic Heterogeneity and Tumor Extent in Glioblastoma

Author

Hu, Leland S., primary, Ning, Shuluo, additional, Eschbacher, Jennifer M., additional, Gaw, Nathan, additional, Dueck, Amylou C., additional, Smith, Kris A., additional, Nakaji, Peter, additional, Plasencia, Jonathan, additional, Ranjbar, Sara, additional, Price, Stephen J., additional, Tran, Nhan, additional, Loftus, Joseph, additional, Jenkins, Robert, additional, O’Neill, Brian P., additional, Elmquist, William, additional, Baxter, Leslie C., additional, Gao, Fei, additional, Frakes, David, additional, Karis, John P., additional, Zwart, Christine, additional, Swanson, Kristin R., additional, Sarkaria, Jann, additional, Wu, Teresa, additional, Mitchell, J. Ross, additional, and Li, Jing, additional

Published
2015
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44. Synthesis and evaluation of azapeptide-derived inhibitors of serine and cysteine proteases

Author

Rinker James M, Bruce Gauvin, Roland E. Dolle, Mark A. Ator, Jill S. Gregory, Alex L. Harris, Todd L. Graybill, and Mitchell J. Ross

Subjects
Proteases, Protease, biology, Chemistry, medicine.medical_treatment, Organic Chemistry, Clinical Biochemistry, Pharmaceutical Science, Calpain, Biochemistry, Cathepsin B, Deubiquitinating enzyme, Cathepsin C, Serine, Drug Discovery, biology.protein, medicine, Molecular Medicine, Molecular Biology, Cysteine
Abstract

Azapeptide analogues ii of the α-halomethyl ketones i were synthesized and evaluated as potential inhibitors of serine and cysteine proteases. Inhibitors ii discriminate between the two classes of protease, demonstrating selectivity for cysteine proteases. Azaglycines 1–3 and 5 displayed time-dependent inactivation (kobs/[I] = 200–1500 M−1s−1) of cathepsin B and calpain.

Published
1992

45. Transcatheter device closure of interatrial septal defects in patients with hypoxia

Author

Frank E. Silvestry, Srihari S. Naidu, Mitchell J. Ross, Howard C. Herrmann, Leonard Ilkhanoff, and Sameer Rohatgi

Subjects
Adult, Male, medicine.medical_specialty, Cardiac Catheterization, medicine.medical_treatment, Heart Septal Defects, Atrial, Prosthesis Implantation, Internal medicine, medicine, Humans, Radiology, Nuclear Medicine and imaging, Hypoxia, Saline, Cardiac catheterization, Aged, Ultrasonography, Aged, 80 and over, Heart septal defect, business.industry, Intermittent hypoxia, Prostheses and Implants, Hypoxia (medical), Middle Aged, medicine.disease, Surgery, Shunting, Heart failure, Patent foramen ovale, Cardiology, Female, medicine.symptom, Cardiology and Cardiovascular Medicine, business
Abstract

Background: There has been growing interest in transcatheter closure of interatrial septal defects (IASDs) for a variety of indications, but reports are limited in patients with hypoxia from right-to-left shunting. Methods: Between August 2000 and October 2004, 181 patients were referred to our institution for elective closure of a patent foramen ovale (PFO) or atrial septal defect (ASD). Among these patients, 10 (5.5%) underwent closure for hypoxia due to persistent or intermittent right-to-left shunting. Clinical evaluation, including echocardiography with color Doppler and agitated saline, was performed in all patients to determine the degree of right-to-left shunting. Defects were closed with Amplatzer (n = 4) or Cardioseal (n = 6) devices, under transesophageal (TEE) or intracardiac echocardiography (ICE) guidance. Results: Mean age was 62.7 years (range: 31–88 years) with 70% female. Characteristics for closure included four patients with persistent hypoxia and six with intermittent hypoxia, including two with platypnea-orthodeoxia syndrome. All patients had echocardiography showing moderate (n = 6) or severe (n = 4) shunting. Patients had significant comorbidities, including chronic lung disease requiring supplemental oxygen (n = 5) and congestive heart failure (n = 2). TEE guidance was used in three patients, and ICE was performed in the remainder. Mean closure device diameter was 27 mm. Mean preprocedural arterial oxygen saturation of 86.7% improved to 95.9% immediately after closure, with color Doppler and agitated saline revealing the absence of (n = 5) or mild (n = 5) shunting. In-hospital major complications were limited to one patient with a transient ischemic attack after an initially unsuccessful closure attempt. Conclusions: Percutaneous closure of IASDs in a heterogeneous group of patients with hypoxia can be safely and effectively performed. The procedure results in immediate arterial saturation improvement and reduced right-to-left shunting.

Published
2005

46. Healthcare Intelligence: Turning Data into Knowledge

Author

Yang, Hui, primary, Kundakcioglu, Erhun, additional, Li, Jing, additional, Wu, Teresa, additional, Mitchell, J. Ross, additional, Hara, Amy K., additional, Pavlicek, William, additional, Hu, Leland S., additional, Silva, Alvin C., additional, Zwart, Christine M., additional, Tunc, Sait, additional, Alagoz, Oguzhan, additional, Burnside, Elizabeth, additional, Chaovalitwongse, W. Art, additional, Presnyakov, Georgiy, additional, Cao, Yulian, additional, Sujitnapitsatham, Sirirat, additional, Won, Daehan, additional, Madhyastha, Tara, additional, Weaver, Kurt E., additional, Borghesani, Paul R., additional, Grabowski, Thomas J., additional, Shu, Lianjie, additional, Ling, Man Ho, additional, Wong, Shui-Yee, additional, and Tsui, Kwok-Leung, additional

Published
2014
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47. Angiographic variables predict increased risk for adverse ischemic events after coronary stenting with glycoprotein IIb/IIIa inhibition: results from the TARGET trial

Author

Mitchell J, Ross, Howard C, Herrmann, David J, Moliterno, James C, Blankenship, Laura, Demopoulos, Peter M, DiBattiste, Stephen G, Ellis, Ziyad, Ghazzal, Jack L, Martin, Jennifer, White, and Eric J, Topol

Subjects
Male, Abciximab, Antibodies, Monoclonal, Platelet Glycoprotein GPIIb-IIIa Complex, Middle Aged, Coronary Angiography, Risk Assessment, Immunoglobulin Fab Fragments, Double-Blind Method, Tirofiban, Ischemia, Predictive Value of Tests, Multivariate Analysis, Humans, Tyrosine, Female, Stents, Angioplasty, Balloon, Coronary, Platelet Aggregation Inhibitors
Abstract

We sought to assess whether pre-procedural angiographic characteristics are associated with adverse clinical outcomes after coronary stenting with glycoprotein IIb/IIIa inhibition.Ischemic complications after balloon angioplasty are associated with pre- and post-procedural angiographic variables. However, in the current era of stenting with IIb/IIIa inhibition, it is unknown whether angiographic features assessed before intervention confer an increased risk of adverse procedural and subsequent clinical outcomes.In the Do Tirofiban and ReoPro Give Similar Efficacy Outcomes? Trial (TARGET), 4,809 patients undergoing planned stenting were randomized to tirofiban or abciximab. Baseline demographic, clinical, and angiographic variables were obtained. Clinical end points were recorded at 30 days and six months. The relationship between angiographic variables and adverse clinical outcomes was assessed.Patients with the combination of thrombus, lesion eccentricity, and lesion length20 mm had a 21.4% composite incidence of death, myocardial infarction, or urgent target vessel revascularization (TVR) at 30 days, compared with 4.2% in those patients without these high-risk features (hazard ratio [HR] 3.24, p0.001). After adjustment, the risk was independently associated with thrombus (HR 1.40, p = 0.034), eccentricity (HR 1.67, p0.001), and lesion length20 mm (HR 1.89, p0.001). The risk of six-month TVR was independently associated with left anterior descending coronary artery lesions (HR 1.46, p0.001), restenotic lesions at baseline (HR 1.58, p = 0.006), and lesion length (HR 1.19, p = 0.03).Patients with thrombus, eccentric lesions, or lesion length20 mm are at high risk for ischemic outcomes after coronary stenting, despite IIb/IIIa inhibition. Further research into novel anti-thrombotic therapies or procedural strategies is necessary for these patients.

Published
2003

48. Synthesis and evaluation of peptidyl vicinal tricarbonyl monohydrates as inhibitors of hydrolytic enzymes

Author

Harry H. Wasserman, Bruce Gomes, Mitchell J. Ross, Patricia Power, and David S. Ennis

Subjects
Serine, chemistry.chemical_classification, chemistry.chemical_compound, Hydrolysis, Dipeptide, Enzyme, Chemistry, Ylide, Stereochemistry, Carboxylic acid, Organic Chemistry, Tripeptide, Vicinal
Abstract

Peptidyl vicinal tricarbonyls, prepared from N-protected di- and tripeptides by reaction of the carboxylic acid residues with ylides followed by oxidation, have been shown to be potent inhibitors of serine proteases

Published
1993

49. Corynebacterium jeikeium native valve endocarditis following femoral access for coronary angiography

Author

Mitchell J. Ross, Alan Lisbon, George Sakoulas, William E. Cohn, and Warren J. Manning

Subjects
Microbiology (medical), Aortic valve, Coronary angiography, medicine.medical_specialty, Vascular access, Corynebacterium, Coronary Angiography, Fatal Outcome, Corynebacterium jeikeium, Femoral access, Internal medicine, medicine, Endocarditis, Humans, Femur, Native Valve Endocarditis, biology, Corynebacterium Infections, business.industry, Endocarditis, Bacterial, Middle Aged, medicine.disease, biology.organism_classification, Infectious Diseases, medicine.anatomical_structure, Cardiology, Female, business, Complication
Abstract

We present a unique case of rapidly fatal native aortic-valve endocarditis due to Corynebacterium jeikeium, with inoculation as a complication of repeated femoral vascular access for coronary angiography.

Published
2000

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